Enter a job title or keyword

Forward Deployed AI Engineer

Tekfortune

Job Location:

Chicago, IL - USA

Monthly Salary: Not provided by the employer

Posted: 29 September 2026 (Yesterday)

Application Deadline: 27 December 2026

Vacancies: 1 Vacancy

Job Summary

Tekfortune is a fast-growing consulting firm specialized in permanent contract & project-based staffing services for worlds leading organizations in a broad range of this quickly changing economic landscape virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps our staffing experts can help you find the best job for you.

Job Position: Forward Deployed AI Engineer
Location: Chicago IL or Houston TX

Job Description
Key Responsibilities

Architecture Design: Build enterprise Generative and Agentic AI platforms featuring high-performance RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.
Agent Orchestration: Define multi-agent collaboration patterns memory management and autonomous planning frameworks.
Governance & Security: Implement data privacy compliance risk mitigation and evaluation guardrails across all AI touchpoints.
Cross-functional Leadership: Guide and mentor engineering teams run discovery workshops with stakeholders and define reusable deployment patterns.

Technical Stack:

Retrieval-Augmented Generation (RAG) pipelines semantic caching and context window optimization.
Function calling tool use and structured data extraction schemas.
Evaluation metrics tracing and hallucination reduction guardrails.
Designing agentic-first workflows and autonomous decision loops.
Multi-agent coordination patterns (supervisor-worker decentralized collaboration stateful graphs).
Frameworks like LangChain/LangGraph/Bedrock Core Runtime for state and memory management.
Emerging interoperability standards like Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.
Vector databases (e.g. Milvus Amazon Aurora PostgreSQL) for high -speed similarity search.
Data pipelines and embedding generation workflows using Python FastAPI or Apache Spark.
Cloud-native deployment on platforms like AWS (Amazon Bedrock Lambda EKS SageMaker S3 RDS DocumentDB)
Containerization and orchestration tools including Docker and Kubernetes.
CI/CD pipelines for automated testing of non-deterministic AI outputs.
Observability and logging pipelines for tracking agent token usage latency and failure states.
Responsible AI frameworks data privacy compliance and bias mitigation guardrails.